Theta-Modulated Gamma-Band Synchronization Among Activated Regions During a Verb Generation Task

Expressive language is complex and involves processing within a distributed network of cortical regions. Functional MRI and magnetoencephalography (MEG) have identified brain areas critical for expressive language, but how these regions communicate across the network remains poorly understood. It is thought that synchronization of oscillations between neural populations, particularly at a gamma rate (>30 Hz), underlies functional integration within cortical networks. Modulation of gamma rhythms by theta-band oscillations (4–8 Hz) has been proposed as a mechanism for the integration of local cell coalitions into large-scale networks underlying cognition and perception. The present study tested the hypothesis that these oscillatory mechanisms of functional integration were present within the expressive language network. We recorded MEG while subjects performed a covert verb generation task. We localized activated cortical regions using beamformer analysis, calculated inter-regional phase locking between activated areas, and measured modulation of inter-regional gamma synchronization by theta phase. The results show task-dependent gamma-band synchronization among regions activated during the performance of the verb generation task, and we provide evidence that these transient and periodic instances of high-frequency connectivity were modulated by the phase of cortical theta oscillations. These findings suggest that oscillatory synchronization and cross-frequency interactions are mechanisms for functional integration among distributed brain areas supporting expressive language processing

Variation in functional connectivity along anterior-to-posterior intraparietal sulcus, and relationship with age across late childhood and adolescence

The intraparietal sulcus (IPS), a region in the dorsal attention network (DAN), has been implicated in multi-sensory attention and working memory. Working memory and attention develop across childhood; changes in functional connectivity within the DAN may relate to this maturation. Previous findings regarding fronto-parietal intrinsic functional connectivity age-effects were mixed. Our study aimed to circumvent limitations of previous work using a large cross-sectional sample, 183 typically developing participants 6.5–20 years, from the Autism Brain Imaging Data Exchange, and seed regions along the anterior-to-posterior axis of the IPS. These seeds, IPS0-4, were entered into functional connectivity models. Group-level models investigated differential connectivity along the IPS and relationships with age. Anterior IPS3/4 exhibited greater connectivity with sensorimotor/pre-motor regions. Posterior IPS0/1 demonstrated greater connectivity with dorsal and ventral visual regions. Positive age-effects were found between IPS3-4 and visual regions. Negative age-effects were found between IPS and superior parietal and medial orbitofrontal cortices. Follow-up region of interest analyses were used to estimate age-effects for DAN and anticorrelated default mode network regions. Results suggest age-effects on IPS functional connectivity are relatively modest, and may differ pre- and across-adolescence. Studying typical age-related connectivity variability within this network may help to understand neurodevelopmental disorders marked by impaired attention

Circumscribed interests in adolescents with Autism Spectrum Disorder: A look beyond trains, planes, and clocks

<div><p>Adolescence is a unique developmental period, characterized by physical and emotional growth and significant maturation of cognitive and social skills. For individuals with Autism Spectrum Disorder (ASD), it is also a vulnerable period as cognitive and social skills can deteriorate. Circumscribed interests (CIs), idiosyncratic areas of intense interest and focus, are a core symptom of ASD that may be associated with social development. Yet, relatively little is known about the expression of CIs in adolescents with ASD. Many studies investigating CIs have used images depicting items of special interest; however, it is not clear how images should be customized for adolescent studies. The goal of this study was to gain insight into the types of images that may be appropriate for studies of CIs in adolescents with ASD. To this end, we used a mixed methods design that included, 1) one-on-one interviews with 10 adolescents (4 with ASD and 6 TD), to identify categories of images that were High Autism Interest (‘HAI’) or High Typically Developing Interest (‘HTD’), and 2) an online survey taken by fifty-three adolescents with ASD (42 male) and 135 typically developing (TD) adolescents (55 male) who rated how much they liked 105 ‘HAI’ and ‘HTD’ images. Although we found a significant interaction between ‘HAI’ and ‘HTD’ categories and diagnosis, neither group significantly preferred one category over the other, and only one individual category ('Celebrities') showed a significant group effect, favored by TD adolescents. Males significantly preferred ‘HAI’ images relative to females, and TD adolescents significantly preferred images with social content relative to adolescents with ASD. Our findings suggest that studies investigating affective or neural responses to CI-related stimuli in adolescents should consider that stereotypical ASD interests (e.g. trains, gadgets) may not accurately represent individual adolescents with ASD, many of whom show interests that overlap with TD adolescents (e.g. video games).</p></div

Image viewing times based on diagnosis.

<p>(A) Mean picture viewing times for individual picture categories based on diagnosis. Trend-level effects were seen for ‘Money’, ‘Celebrities’, ‘Ports’, and ‘Room Designs’ categories. (B) Mean picture viewing times for ‘HAI’ and ‘HTD’ super-categories. We found a significant effect of diagnosis on ‘HAI’ images. (C) Mean picture viewing times for 'Social' and 'Non-Social' images. 'Non-Social' images were viewed significantly longer by ASD adolescents, relative to TD adolescents. “+” = <i>p</i> < 0.05 uncorrected; “*" = <i>p</i> < 0.05 corrected for multiple comparisons. Error bars depict standard deviation.</p

Image “liking” ratings based on diagnosis.

<p>(A) Mean picture ratings for individual picture categories based on diagnosis. Only the 'Celebrities' category showed a significant effect of diagnosis, while trend-level effects were seen for ‘Sports’, ‘Lego’ and ‘Complex Foods’ categories. (B) Mean picture ratings for ‘HAI’ and ‘HTD’ super-categories. We found a significant interaction between category and diagnosis, though neither category showed a significant group difference. (C) Mean picture ratings for 'Social' and 'Non-Social' grouped-categories. We found a significant interaction between category and diagnosis, and that 'Social' images were significantly preferred by TD adolescents, relative to adolescents with ASD. “+” = <i>p</i> < 0.05 uncorrected; “*" = <i>p</i> < 0.05 corrected for multiple comparisons. Error bars depict standard deviation.</p

Image “liking” ratings based on gender.

<p>(A) Mean picture ratings for individual picture categories based on gender. ‘Machines’, ‘Sports’, and ‘Gadgets’ categories showed a significant effect of gender, while trend-level effects were seen for ‘Animations’, ‘Room Designs’, ‘Lego’, ‘Art Photos’, and ‘Money’ categories. (B) Mean picture ratings for ‘HAI’ and ‘HTD’ super-categories. Males significantly preferred ‘HAI’ images, relative to females. (C) Mean picture ratings for ‘Social’ and ‘Non-Social’ grouped-categories; ‘Social’ images were significantly preferred by males. “+” = <i>p</i> < 0.05 uncorrected; “*" = <i>p</i> < 0.05 corrected for multiple. Error bars depict standard deviation.</p

Correlation between “liking” ratings and age.

<p>(A) Correlations for individual picture ratings based on age. We found that the picture categories ‘Celebrities’, ‘Female Sports’, ‘Complex Foods’, and ‘Nature Scenes’ showed a positive association with age in ANCOVA models, while ‘Machines’, ‘Ports’, ‘Space’, and ‘Money’ showed a negative association with age. (B) Correlations for ‘HAI’ and ‘HTD’ super-categories based on age. A negative correlation with age was found for ‘HAI’ images. (C) Age correlations for ‘Social’ and ‘Non-Social’ images. “+” = <i>p</i> < 0.05 uncorrected; “*" = <i>p</i> < 0.05 corrected for multiple comparisons. Error bars depict standard deviation.</p

Correlation between viewing times for picture categories and age.

<p>(A) Correlations for individual picture viewing times based on age. All categories were found to be viewed longer by younger participants (at significant or trend-level). (B) Age correlations for ‘HAI’ and ‘HTD’ viewing times; younger participants viewed images longer. (C) Age correlations for ‘Social’ and ‘Non-Social’ viewing times; younger participants viewed both ‘Social’ and ‘Non-Social’ images longer than older participants. “+” = <i>p</i> < 0.05 uncorrected; “*" = <i>p</i> < 0.05 corrected for multiple comparisons. Error bars depict standard deviation.</p

Example question from online survey.

<p>(A) Example of an image from the ‘Nature’ category found in the online survey. After viewing the picture for as long as they liked, participants pressed the “Next” button to move on to the next page. (B) After pressing the “Next” button from the previous page, participants were asked how much they liked the image by rating the picture on a scale of 0–7. After rating the image, the participants moved on to the next image by pressing the “Next” button below. Font size has been enlarged from the actual survey for legibility.</p

Image viewing times based on gender.

<p>(A) Mean picture viewing time for individual picture categories based on gender. Trend-level effects were seen for ‘Travel’, ‘Space’, ‘Videos’, ‘Machines’, and ‘Gadgets’. (B) Mean picture viewing times for ‘HAI’ and ‘HTD' images. ‘HAI’ images were viewed significantly longer by males, relative to females. (C) Mean picture viewing times for ‘Social’ and ‘Non-Social’ images. We found a significant interaction between category and gender, however within each gender category was not significant. “+” = <i>p</i> < 0.05 uncorrected; “*" = <i>p</i> < 0.05 corrected for multiple comparisons. Error bars depict standard deviation.</p